Distance Regularized Level Set Evolution and Its Application to Image Segmentation

作者：Chunming Li, Chenyang Xu, Changfeng Gui, Martin D Fox · 发表于：IEEE Transactions on Image Processing · 年份：2010 · DOI：10.1109/tip.2010.2069690 · 被引用次数：2127 · 研究领域：Medical Image Segmentation Techniques、Reservoir Engineering and Simulation Methods、Advanced Numerical Methods in Computational Mathematics

Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitializatio...